Ok, I admit had help from OpenAi with this. But what I “helped” put together I think can greatly improve the results and costs of using OpenAi within your apps and plugins, specially for those looking to guide internal prompts for plugins… @ruv
I’d like to introduce you to two important parameters that you can use with OpenAI’s GPT API to help control text generation behavior: temperature and top_p sampling.
- These parameters are especially useful when working with GPT for tasks such as code generation, creative writing, chatbot responses, and more.
- Temperature is a parameter that controls the “creativity” or randomness of the text generated by GPT-3. A higher temperature (e.g., 0.7) results in more diverse and creative output, while a lower temperature (e.g., 0.2) makes the output more deterministic and focused.
- In practice, temperature affects the probability distribution over the possible tokens at each step of the generation process. A temperature of 0 would make the model completely deterministic, always choosing the most likely token.
Next, let’s discuss top_p sampling (also known as nucleus sampling):
- Top_p sampling is an alternative to temperature sampling. Instead of considering all possible tokens, GPT-3 considers only a subset of tokens (the nucleus) whose cumulative probability mass adds up to a certain threshold (top_p).
- For example, if top_p is set to 0.1, GPT-3 will consider only the tokens that make up the top 10% of the probability mass for the next token. This allows for dynamic vocabulary selection based on context.
Both temperature and top_p sampling are powerful tools for controlling the behavior of GPT-3, and they can be used independently or together when making API calls. By adjusting these parameters, you can achieve different levels of creativity and control, making them suitable for a wide range of applications.
To give you an idea of how these parameters can be used in different scenarios, here’s a table with example values:
|Generates code that adheres to established patterns and conventions. Output is more deterministic and focused. Useful for generating syntactically correct code.
|Generates creative and diverse text for storytelling. Output is more exploratory and less constrained by patterns.
|Generates conversational responses that balance coherence and diversity. Output is more natural and engaging.
|Code Comment Generation
|Generates code comments that are more likely to be concise and relevant. Output is more deterministic and adheres to conventions.
|Data Analysis Scripting
|Generates data analysis scripts that are more likely to be correct and efficient. Output is more deterministic and focused.
|Exploratory Code Writing
|Generates code that explores alternative solutions and creative approaches. Output is less constrained by established patterns.
I hope this introduction helps you understand the basics of temperature and top_p sampling in the context of OpenAI’s GPI API. If you have any questions or experiences to share, feel free to comment below!